{"id":"W2087867734","doi":"10.1109/allerton.2012.6483366","title":"Least-squares based adaptive source localization by mobile agents","year":2012,"lang":"en","type":"article","venue":"","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Forgetting; Recursive least squares filter; Convergence (economics); Least-squares function approximation; Algorithm; Computer science; Stability (learning theory); Noise (video); Mathematical optimization; Least mean squares filter; Adaptive filter; Mathematics; Artificial intelligence; Statistics; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004437234,0.0004702127,0.0005223976,0.0003065489,0.0002716577,0.0005030622,0.0007645204,0.0007403423,0.0006195593],"category_scores_gemma":[0.001760487,0.0003165618,0.0003683041,0.0003453683,0.0006286507,0.0007034748,0.0007859662,0.0007203374,0.0003360648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002636686,"about_ca_system_score_gemma":0.0004207551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00196277,"about_ca_topic_score_gemma":0.001403136,"domain_scores_codex":[0.9997163,0.00008690656,0.00001399924,0.00006742957,0.00009510354,0.00002031329],"domain_scores_gemma":[0.9995369,0.0002086574,0.00006110092,0.00005253017,0.0001237389,0.00001710019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001993288,0.00005200259,0.0009384237,0.0001173342,0.00008735196,0.0002106601,0.0002168302,0.7915947,0.03749994,0.01468181,0.000938639,0.1534629],"study_design_scores_gemma":[0.0000150325,0.0000409848,0.00008209537,0.000004075261,0.000006736267,0.00002460218,0.00001018148,0.9938167,0.003153101,0.001911444,0.0009279812,0.000007008866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01275853,0.0001438966,0.9861869,0.00007262301,0.00002502499,0.00001413238,0.000005265547,0.0001952354,0.0005983425],"genre_scores_gemma":[0.7096807,0.0003732316,0.2860573,0.0000870644,0.00004688117,0.0001049026,0.00004249238,0.00004627185,0.003561084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00196277,"threshold_uncertainty_score":0.003902733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01706487656473811,"score_gpt":0.2389257880554191,"score_spread":0.221860911490681,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}